

import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}





class 					__lowerCAmelCase	(   __lowercase				):
  _a		     =    """bart"""
  _a		     =    ["""past_key_values"""]
  _a		     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




  def __init__(							self				,  lowerCAmelCase=50_265				,  lowerCAmelCase=1_024				,  lowerCAmelCase=12				,  lowerCAmelCase=4_096				,  lowerCAmelCase=16				,  lowerCAmelCase=12				,  lowerCAmelCase=4_096				,  lowerCAmelCase=16				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=1_024				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=0.0				,  lowerCAmelCase=False				,  lowerCAmelCase=True				,  lowerCAmelCase=3				,  lowerCAmelCase=1				,  lowerCAmelCase=0				,  lowerCAmelCase=2				,  lowerCAmelCase=True				,  lowerCAmelCase=2				,  lowerCAmelCase=2				,  **lowerCAmelCase				,  )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=max_position_embeddings
        _lowercase      			=d_model
        _lowercase      			=encoder_ffn_dim
        _lowercase      			=encoder_layers
        _lowercase      			=encoder_attention_heads
        _lowercase      			=decoder_ffn_dim
        _lowercase      			=decoder_layers
        _lowercase      			=decoder_attention_heads
        _lowercase      			=dropout
        _lowercase      			=attention_dropout
        _lowercase      			=activation_dropout
        _lowercase      			=activation_function
        _lowercase      			=init_std
        _lowercase      			=encoder_layerdrop
        _lowercase      			=decoder_layerdrop
        _lowercase      			=classifier_dropout
        _lowercase      			=use_cache
        _lowercase      			=encoder_layers
        _lowercase      			=scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowerCAmelCase				,  pad_token_id=lowerCAmelCase				,  bos_token_id=lowerCAmelCase				,  eos_token_id=lowerCAmelCase				,  is_encoder_decoder=lowerCAmelCase				,  decoder_start_token_id=lowerCAmelCase				,  forced_eos_token_id=lowerCAmelCase				,  **lowerCAmelCase				,  )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'				,  lowerCAmelCase   ):
              _lowercase      			=self.bos_token_id
              warnings.warn(
                  F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                  'The config can simply be saved and uploaded again to be fixed.'   )





class 					__lowerCAmelCase	(   __lowercase				):




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
              _lowercase      			=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]   )

              if self.use_past:
                    _lowercase      			={0: "batch"}
                    _lowercase      			={0: "batch", 1: "past_decoder_sequence + sequence"}
              else:
                    _lowercase      			={0: "batch", 1: "decoder_sequence"}
                    _lowercase      			={0: "batch", 1: "decoder_sequence"}

              if self.use_past:
                    self.fill_with_past_key_values_(lowerCAmelCase				,  direction='inputs'   )
        elif self.task == "causal-lm":
              # TODO: figure this case out.
              _lowercase      			=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]   )
              if self.use_past:
                    _lowercase      			=self.num_layers
                    for i in range(lowerCAmelCase   ):
                          _lowercase      			={0: "batch", 2: "past_sequence + sequence"}
                          _lowercase      			={0: "batch", 2: "past_sequence + sequence"}
        else:
              _lowercase      			=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ]   )

        return common_inputs




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
              _lowercase      			=super().outputs
        else:
              _lowercase      			=super(lowerCAmelCase				,  self   ).outputs
              if self.use_past:
                    _lowercase      			=self.num_layers
                    for i in range(lowerCAmelCase   ):
                          _lowercase      			={0: "batch", 2: "past_sequence + sequence"}
                          _lowercase      			={0: "batch", 2: "past_sequence + sequence"}
        return common_outputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        _lowercase      			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )

        # Generate decoder inputs
        _lowercase      			=seq_length if not self.use_past else 1
        _lowercase      			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        _lowercase      			=dict(**lowerCAmelCase				,  **lowerCAmelCase   )

        if self.use_past:
              if not is_torch_available():
                    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
              else:
                    import torch
              _lowercase      			=common_inputs["input_ids"].shape
              _lowercase      			=common_inputs["decoder_input_ids"].shape[1]
              _lowercase      			=self.num_attention_heads
              _lowercase      			=(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              _lowercase      			=decoder_seq_length + 3
              _lowercase      			=(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              _lowercase      			=torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase				,  lowerCAmelCase   )]				,  dim=1   )

              _lowercase      			=[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              _lowercase      			=self.num_layers
              _lowercase      			=min(lowerCAmelCase				,  lowerCAmelCase   )
              _lowercase      			=max(lowerCAmelCase				,  lowerCAmelCase   ) - min_num_layers
              _lowercase      			="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

              for _ in range(lowerCAmelCase   ):
                    common_inputs["past_key_values"].append(
                        (
                            torch.zeros(lowerCAmelCase   ),
                            torch.zeros(lowerCAmelCase   ),
                            torch.zeros(lowerCAmelCase   ),
                            torch.zeros(lowerCAmelCase   ),
                        )   )
              # TODO: test this.
              _lowercase      			=encoder_shape if remaining_side_name == "encoder" else decoder_shape
              for _ in range(lowerCAmelCase				,  lowerCAmelCase   ):
                    common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   ))   )
        return common_inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        _lowercase      			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )

        if self.use_past:
              if not is_torch_available():
                    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
              else:
                    import torch
              _lowercase      			=common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              _lowercase      			=seqlen + 2
              _lowercase      			=self.num_layers
              _lowercase      			=self.num_attention_heads
              _lowercase      			=(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              _lowercase      			=common_inputs["attention_mask"].dtype
              _lowercase      			=torch.cat(
                  [common_inputs['attention_mask'], torch.ones(lowerCAmelCase				,  lowerCAmelCase				,  dtype=lowerCAmelCase   )]				,  dim=1   )
              _lowercase      			=[
                  (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(lowerCAmelCase   )
              ]
        return common_inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        _lowercase      			=compute_effective_axis_dimension(
            lowerCAmelCase				,  fixed_dimension=OnnxConfig.default_fixed_batch				,  num_token_to_add=0   )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        _lowercase      			=tokenizer.num_special_tokens_to_add(lowerCAmelCase   )
        _lowercase      			=compute_effective_axis_dimension(
            lowerCAmelCase				,  fixed_dimension=OnnxConfig.default_fixed_sequence				,  num_token_to_add=lowerCAmelCase   )

        # Generate dummy inputs according to compute batch and sequence
        _lowercase      			=[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
        _lowercase      			=dict(tokenizer(lowerCAmelCase				,  return_tensors=lowerCAmelCase   )   )
        return common_inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
              _lowercase      			=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowerCAmelCase				,  batch_size=lowerCAmelCase				,  seq_length=lowerCAmelCase				,  is_pair=lowerCAmelCase				,  framework=lowerCAmelCase   )

        elif self.task == "causal-lm":
              _lowercase      			=self._generate_dummy_inputs_for_causal_lm(
                  lowerCAmelCase				,  batch_size=lowerCAmelCase				,  seq_length=lowerCAmelCase				,  is_pair=lowerCAmelCase				,  framework=lowerCAmelCase   )
        else:
              _lowercase      			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  lowerCAmelCase				,  batch_size=lowerCAmelCase				,  seq_length=lowerCAmelCase				,  is_pair=lowerCAmelCase				,  framework=lowerCAmelCase   )

        return common_inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
              _lowercase      			=super()._flatten_past_key_values_(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        else:
              _lowercase      			=super(lowerCAmelCase				,  self   )._flatten_past_key_values_(
                  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )




from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)